Structured backward error analysis of linearized structured polynomial eigenvalue problems Articles uri icon

publication date

  • May 2019

start page

  • 1189

end page

  • 1228

issue

  • 317

volume

  • 88

international standard serial number (ISSN)

  • 0025-5718

electronic international standard serial number (EISSN)

  • 1088-6842

abstract

  • We start by introducing a new class of structured matrix polynomials, namely, the class of M-A-structured matrix polynomials, to provide a common framework for many classes of structured matrix polynomials that are important in applications: the classes of (skew-)symmetric, (anti-) palindromic, and alternating matrix polynomials. Then, we introduce the families of M-A-structured strong block minimal bases pencils and of M-A-structured block Kronecker pencils, which are particular examples of block minimal bases pencils recently introduced by Dopico, Lawrence, Perez and Van Dooren, and show that any M-A-structured odd-degree matrix polynomial can be strongly linearized via an M-A-structured block Kronecker pencil. Finally, for the classes of (skew-)symmetric, (anti-)palindromic, and alternating odd-degree matrix polynomials, the M-A-structured framework allows us to perform a global and structured backward stability analysis of complete structured polynomial eigenproblems, regular or singular, solved by applying to a M-A-structured block Kronecker pencil a structurally backward stable algorithm that computes its complete eigenstructure, like the palindromic-QR algorithm or the structured versions of the staircase algorithm. This analysis allows us to identify those M-A-structured block Kronecker pencils that yield a computed complete eigenstructure which is the exact one of a slightly perturbed structured matrix polynomial. These pencils include (modulo permutations) the well-known block-tridiagonal and block-anti-tridiagonal structure-preserving linearizations. Our analysis incorporates structure to the recent (unstructured) backward error analysis performed for block Kronecker linearizations by Dopico, Lawrence, Perez and Van Dooren, and share with it its key features, namely, it is a rigorous analysis valid for finite perturbations, i.e., it is not a first order analysis, it provides precise bounds, and it is valid simultaneously for a large class of structure-pre

keywords

  • structured backward error analysis; complete polynomial eigenproblems; structured matrix polynomials; structure-preserving linearizations; mobius transformations; matrix pertubation theory; dual minimal bases; hermitian matrix polynomials; fiedler pencils; vector-spaces; minimal bases; eigenstructure; perturbation; algorithm; recovery; even; ax